1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
\name{rmad}
\alias{rmad}
\title{RMAD correlation matrix}
\description{
Compute the RMAD robust correlation matrix proposed in Serra et
al. (2018) based on the robust correlation coefficient proposed in
Pasman and Shevlyakov (1987).
}
\usage{
rmad(x , y = NULL, na.rm = FALSE , even.correction = FALSE)
}
\arguments{
\item{x}{
A numeric vector, a matrix or a data.frame. If \code{x} is a matrix
or a data.frame, rows of \code{x} correspond to sample units
and columns correspond to variables. If \code{x} is a numerical
vector, and \code{y} is not \code{NULL}, the RMAD correlation
coefficient between \code{x} and \code{y} is computed. Categorical
variables are not allowed.
}
\item{y}{
A numerical vector if not \code{NULL}. If both \code{x} and \code{y}
are numerical vectors, the RMAD correlation coefficient between
\code{x} and \code{y} is computed.
}
\item{na.rm}{
A logical value, if \code{TRUE} sample observation
containing \code{NA} values are excluded (see \emph{Details}).
}
\item{even.correction}{
A logical value, if \code{TRUE} a correction
for the calculation of the medians is applied to reduce the bias
when the number of samples even (see \emph{Details}).
}
}
\details{
The \code{rmad} function computes the correlation matrix based on the
pairwise robust correlation coefficient of Pasman and Shevlyakov
(1987). This correlation coefficient is based on repeated median
calculations for all pairs of variables. This is a computational
intensive task when the number of variables (that is \code{ncol(x)})
is large.
The software is optimized for large dimensional data sets, the median
is approximated as the central observation obtained based on the
\emph{introselect} sorting algorithm of Musser (1997) implemented in
Fortran 95 language. For small samples this may be a crude
approximation, however, it makes the computational cost feasible for
high-dimensional data sets. With the option \code{even.correction
= TRUE} a correction is applied to reduce the bias for data sets with
an even number of samples. Although \code{even.correction = TRUE}
has a small computational cost for each pair of variables, it is
suggested to use the default \code{even.correction = FALSE} for large
dimensional data sets.
The function can handle a data matrix with missing values (\code{NA}
records). If \code{na.rm = TRUE} then missing values are handled by
casewise deletion (and if there are no complete cases, an error is
returned). In practice, if \code{na.rm = TRUE} all rows of
\code{x} that contain at least an \code{NA} are removed.
Since the software is optimized to work with high-dimensional data sets,
the output RMAD matrix is packed into a storage efficient format
using the \code{"dspMatrix"} S4 class from the \code{\link{Matrix}}
package. The latter is specifically designed for dense real symmetric
matrices. A sparse correlation matrix can be obtained applying
thresholding using the \code{\link{rsc_cv}} and \code{\link{rsc}}.
}
\value{
\item{If \code{x} is a matrix or a data.frame}{
Returns a correlation matrix of class \code{"dspMatrix"} (S4 class object)
as defined in the \code{\link{Matrix}} package.
}
\item{If \code{x} and \code{y} are numerical vectors}{
Returns a numerical value, that is the RMAD correlation coefficient
between \code{x} and \code{y}.
}
}
\section{References}{
Musser, D. R. (1997). Introspective sorting and selection algorithms.
\emph{Software: Practice and Experience}, 27(8), 983-993.
Pasman,V. and Shevlyakov,G. (1987). Robust methods of estimation of
correlation coefficient. \emph{Automation Remote Control}, 48, 332-340.
Serra, A., Coretto, P., Fratello, M., and Tagliaferri, R. (2018).
Robust and sparsecorrelation matrix estimation for the analysis of
high-dimensional genomics data. \emph{Bioinformatics}, 34(4), 625-634.
doi: 10.1093/bioinformatics/btx642
}
\seealso{
\code{rsc_cv}, \code{rsc}
}
\examples{
## simulate a random sample from a multivariate Cauchy distribution
set.seed(1)
n <- 100 # sample size
p <- 7 # dimension
dat <- matrix(rt(n*p, df = 1), nrow = n, ncol = p)
colnames(dat) <- paste0("Var", 1:p)
## compute the rmad correlation coefficient between dat[,1] and dat[,2]
a <- rmad(x = dat[,1], y = dat[,2])
## compute the RMAD correlaiton matrix
b <- rmad(x = dat)
b
}
|
